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Terrance Savitsky

Terrance Savitsky

 

Terrance Savitsky, Ph.D.

Senior Research Mathematical Statistician, Office of Survey Methods Research
Education:
  • Rice University, M.A., 2009, Statistics
  • Rice University, Ph.D., 2010, Statistics
  • Cornell University, M.I.L.R, Human Resources
  • University of Texas at Austin, M.B.A., Corporate Strategy
  • Rice University, B.S.M.E, Mechanical Engineering
Fields of Interest:
  • Bayesian model estimation under complex sampling to achieve frequentist consistency and correct uncertainty quantification with respect to the joint distribution governing population generation and the taking of a complex sample.
  • Bayesian synthetic data estimation for the achievement of a formal privacy guarantee that well-preserves the utility of the closely-held data distribution.
  • Bayesian functional data models for small domain estimation for time- and spatially-indexed data.
  • Bayesian nonparametric modeling, including Gaussian processes, continuous random measures and adaptive auto-regressive prior formulations.
Professional Experience:
  • Senior Research Mathematical Statistician, Bureau of Labor Statistics, 2021 -present
  • Research Mathematical Statistician, Bureau of Labor Statistics, 2013 - 2021
  • Associate Statistician, RAND Corporation, 2010- 2013
Selected Publications and Working Papers
  • Savitsky, T.D., Williams, M.R. 2022. Pseudo Bayesian Mixed Effects Model Estimation Under Informative Sampling, Journal of Official Statistics.
  • Hu, J., Savitsky, T.D., Williams, M.R., 2022. Private Tabular Survey Data Products through Synthetic Microdata Generation. Journal of Survey Statistics and Methodology.
  • Savitsky, T.D., Williams, M.R., Hu, J. 2022. Bayesian Pseudo Posterior Mechanism under Asymptotic Differential Privacy, Journal of Machine Learning Research.
  • Savitsky, T.D., Gershunskaya, J. 2022. Bayesian Small Area Count data Model for Joint Estimation of Point Estimates and Variances. Under Review at: Annals of Applied Statistics.
  • Savitsky, T.D., Gershunskaya, J. 2022. Bayesian Nonparametric Joint Model for Domain Point Estimates and Variances under Biased Observed Variances, Journal of Survey Statistics and Methodology.
  • Hu, J., Savitsky, T.D., Williams, M.R., 2021. Risk-efficient Bayesian Data Synthesis for Privacy Protection. Journal of Survey Statistics and Methodology, https://doi.org/10.1093/jssam/smab013
  • Savitsky, T.D. 2021. Bayesian Dependent Functional Mixture Estimation for Area and Timeindexed Data: An Application for the Prediction of Monthly County Employment, Bayesian Analysis.
  • Hu, J., Savitsky, T.D., 2021. Bayesian Data Synthesis and Disclosure Risk Quantification: An Application to the Consumer Expenditure Surveys. Under Review at: Annals of Applied Statistics.
  • Leon Novelo, L.G., Savitsky, T.D., 2021. Fully Bayesian Mixed Effects Model Estimation under Informative Survey Sampling, To Appear in: Journal of Survey Statistics and Methodology.
  • Gohaniyogi, R., Li, C., Savitsky, T.D., Srivastava, S., 2021. A Divide-and-Conquer Bayesian Approach to Large-Scale Kriging. Submitted to: Statistical Science.
  • Williams, M.R., Savitsky, T.D., 2020. Bayesian Uncertainty Estimation Under Complex Sampling. International Statistical Review.
  • Savitsky, T.D., Gershunskaya, J. 2019. Model Based Screening for Robust Estimation in the presence of deviations from linearity in small domain models, Journal of Survey Statistics and Methodology, https://doi.org/10.1093/jssam/smz004
  • Williams, M.R., Savitsky, T.D., 2018. Bayesian Estimation Under Informative Sampling with Unattenuated Dependence, Bayesian Analysis, doi:10.1214/18-BA1143, https://projecteuclid.org/euclid.ba/1546570987, 21 pages.
  • Leon Novelo, L.G., Savitsky, T.D., 2018. Fully Bayesian Estimation Under Informative Sampling, Electronic Journal of Statistics, Volume 13, Number 1, pp. 1608-1645, https://projecteuclid.org/euclid.ejs/1555466479.
  • Savitsky, T.D., 2018. Bayesian Pseudo Posterior Estimation for Functional Data under Informative Sampling, Under Review at: Electronic Journal of Statistics.
  • Williams, M.R., Savitsky, T.D., 2018. Bayesian Pairwise Estimation Under Dependent Informative Sampling, Electronic Journal of Statistics, Volume 12, Number 1, pp. 1631-1661, https://projecteuclid.org/euclid.ejs/1527300143.
  • Savitsky, T.D. 2018. Bayesian Nonparametric Functional Mixture Estimation for Time-Series Data, With Application to Estimation of State Employment Totals, Journal of Survey Statistics and Methodology, https://doi.org/10.1093/jssam/smy001, 31 pages.
  • Savitsky, T.D., Srivastava, S. 2018. Scalable Bayesian Inference Under Informative Sampling, Scandinavian Journal of Statistics, Volume 45, pp. 534-556. https://doi:10.1111/sjos.
  • Savitsky, T.D., 2017. Bayesian Non-Parametric Multiresolution Estimation for the American Community Survey, Annals of Applied Statistics, Volume 10, Number 4, pp. 2157–2181.
  • Savitsky, T.D., Gershunskaya, J. 2017. Dependent Latent Effects Modeling for Survey Estimation with Application to the Current Employment Statistics Survey, Journal of Survey Statistics and Methodology, https://doi.org/10.1093/jssam/smx021, 22 pages.
  • Savitsky, T.D., 2016. Toth, D., Bayesian Estimation Under Informative Sampling, Electronic Journal of Statistics, Volume 10, Number 1, pp. 1677 - 1708, http://projecteuclid.org/euclid.ejs/1468847267.
  • Savitsky, T.D., 2016. Bayesian Non-Parametric Functional Mixture Estimation for Time-Indexed Functional Data in R, Journal of Statistical Software, Volume 72, Number 2, pp. 1 - 34, https://www.jstatsoft.org/article/view/v072i02 .

 

Last Modified Date: April 19, 2023